*'reduce': returns a Seriesifpossible rather than expanding list-like results. Thisisthe opposite of'expand'. *'broadcast': results will be broadcast to the original shape of the DataFrame, the original indexandcolumns will be retained.Thedefaultbehaviour(None) dependsonthereturnvalueof the appli...
Passing result_type='expand' will expand list-like results to columns of a Dataframe >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand') 0 1 0 1 2 1 1 2 2 1 2 Returning a Series inside the function is similar to passing ``result_type='expand'``. The resulting co...
代码语言:javascript 复制 build = lambda x: pd.DataFrame(x, index=df2.index, columns=df2.columns) cls1 = build(df2.apply(highlight_max, props='cls-1 ', axis=0)) cls2 = build(df2.apply(highlight_max, props='cls-2 ', axis=1, result_type='expand').values) cls3 = build(high...
In [1]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) In [2]: firstlast["First_Name"] = firstlast["String"].str.split(" ", expand=True)[0] In [3]: firstlast["Last_Name"] = firstlast["String"].str.rsplit(" ", expand=True)[1] In [4]: firstla...
可以轻松扩展此操作以使用expand返回 DataFrame。 In [42]: s2.str.split("_", expand=True) Out[42]:0120a b c1c d e2<NA> <NA> <NA>3f g h 当原始Series具有StringDtype时,输出列也将全部是StringDtype。 也可以限制拆分的数量: In [43]: s2.str.split("_", expand=True, n=1) ...
,"value"]].apply(lambdax:process_col1(x["agents"],x["value"]),axis=1,result_type="expand...
import numpy as np import pandas as pd import seaborn as sns titanic = sns.load_dataset('titanic') titanic.pivot_table(index='sex', columns='class') # 默认对所有列进行聚合,这时我们给与values参数,只计算想要的结果 agg = pd.cut(titanic["age"],[0,18,80]) # 对年龄数据列进行分段,便于观...
您可以使用正则表达式来提取xxx (yyy)(yyy)部分，然后重新整形：
df.姓名.str.split(' ', expand=True) 11.把 Series 里的列表转换为 DataFrame df = pd.DataFrame({'列1':['a','b','c'],'列2':[[10,20], [20,30], [30,40]]}) df df_new = df.列2.apply(pd.Series) pd.concat([df,df_new], axis='columns') 12.用多个函数聚合 orders = pd...
把单元格内容转成list df[column]=df[column].str.split(" \n",expand=False) 索引 把索引建为新列 df["column_name"]=df.index 更新筛选后的索引 df.index = range(len(df)) 重设索引 result = result.reset_index() result = result.reset_index(drop=True) ...